2021
Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
Shung D, Huang J, Castro E, Tay JK, Simonov M, Laine L, Batra R, Krishnaswamy S. Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit. Scientific Reports 2021, 11: 8827. PMID: 33893364, PMCID: PMC8065139, DOI: 10.1038/s41598-021-88226-3.Peer-Reviewed Original ResearchConceptsAcute gastrointestinal bleedingRed blood cell transfusionBlood cell transfusionGastrointestinal bleedingHigh-risk patientsCell transfusionRed blood cellsPatient cohortIntensive Care III (MIMIC-III) critical care databaseIntensive care unit staySevere acute gastrointestinal bleedingPacked red blood cellsBlood cellsCommon gastrointestinal causesLaboratory test featuresTime-updated dataIntensive care unitValidation patient cohortCritical care databaseLarge urban hospitalMedical Information MartInternal validation setGastrointestinal causesUnit stayCare unitChallenges of developing artificial intelligence‐assisted tools for clinical medicine
Shung DL, Sung JJY. Challenges of developing artificial intelligence‐assisted tools for clinical medicine. Journal Of Gastroenterology And Hepatology 2021, 36: 295-298. PMID: 33624889, DOI: 10.1111/jgh.15378.Peer-Reviewed Original ResearchConceptsArtificial intelligenceAI toolsClinical decision supportMachine learningData managementMassive amountsDecision supportVibrant ecosystemClinical careComputational toolsRight timeMultiple sourcesQuality of careOperational levelRisk stratificationManometric testingRadiologic imagingClinical managementRight patientRight amountClinical practiceToolCare deliveryVisual findingsAreas of medicineAdvancing care for acute gastrointestinal bleeding using artificial intelligence
Shung DL. Advancing care for acute gastrointestinal bleeding using artificial intelligence. Journal Of Gastroenterology And Hepatology 2021, 36: 273-278. PMID: 33624892, DOI: 10.1111/jgh.15372.Peer-Reviewed Original ResearchConceptsElectronic health recordsAcute gastrointestinal bleedingIntegration of machineHealth recordsNeural network modelGastrointestinal bleedingRisk prediction toolsNeural network-based analysisArtificial intelligenceMachine learningDecision supportRisk patientsNetwork modelReal timeMachineAlgorithmPrediction toolsClinical risk scoreLower gastrointestinal bleedingLow-risk patientsHigh-risk patientsProspective clinical trialsTriage of patientsClinician risk assessmentDelivery of careEarly identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules
Shung D, Tsay C, Laine L, Chang D, Li F, Thomas P, Partridge C, Simonov M, Hsiao A, Tay JK, Taylor A. Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules. Journal Of Gastroenterology And Hepatology 2021, 36: 1590-1597. PMID: 33105045, DOI: 10.1111/jgh.15313.Peer-Reviewed Original ResearchConceptsNatural language processingElectronic health recordsLanguage processingNLP algorithmSystematized NomenclatureReal timeAcute gastrointestinal bleedingBidirectional Encoder RepresentationsDecision rulesEHR-based phenotyping algorithmsGastrointestinal bleedingRisk stratification scoresEncoder RepresentationsData elementsPhenotyping algorithmStratification scoresHealth recordsAlgorithmPhenotyping of patientsEmergency department patientsTime of presentationRisk stratification modelED reviewDeploymentExternal validation
2019
Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding
Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, Laine L. Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2019, 158: 160-167. PMID: 31562847, PMCID: PMC7004228, DOI: 10.1053/j.gastro.2019.09.009.Peer-Reviewed Original ResearchConceptsUpper gastrointestinal bleedingHospital-based interventionsComposite endpointScoring systemRockall scoreGastrointestinal bleedingClinical riskConsecutive unselected patientsLow-risk patientsClinical scoring systemRisk-scoring systemExternal validation cohortCharacteristic curve analysisInternal validation setOutpatient managementUnselected patientsValidation cohortEmergency departmentMedical CenterGreater AUCPatientsAbstractTextCurve analysisEndpointAUCMachine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review
Shung D, Simonov M, Gentry M, Au B, Laine L. Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review. Digestive Diseases And Sciences 2019, 64: 2078-2087. PMID: 31055722, DOI: 10.1007/s10620-019-05645-z.Peer-Reviewed Original ResearchConceptsClinical risk scoreUpper gastrointestinal bleedingGastrointestinal bleedingOutcomes of mortalityRisk scoreSystematic reviewOvert gastrointestinal bleedingAcute gastrointestinal bleedingPrognosis Studies toolRisk of biasFull-text studiesCurrent risk assessment toolsRisk assessment toolHospital stayHemostatic interventionRisk stratificationInclusion criteriaPrognostic performanceHigh riskIndependent reviewersConference abstractsLower riskMedian AUCPatientsMortality
2014
Medical and Surgical Complications of Inflammatory Bowel Disease in the Elderly: A Systematic Review
Shung DL, Abraham B, Sellin J, Hou JK. Medical and Surgical Complications of Inflammatory Bowel Disease in the Elderly: A Systematic Review. Digestive Diseases And Sciences 2014, 60: 1132-1140. PMID: 25501923, DOI: 10.1007/s10620-014-3462-2.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseElderly IBD patientsElderly patientsSystematic reviewIBD patientsSurgical complicationsBowel diseaseInclusion criteriaTumor necrosis factor antagonistsHospital-related complicationsTherapy-related complicationsNecrosis factor antagonistsPouch-anal anastomosisMedication-related complicationsRisk of hospitalHigh-quality studiesMeta-Analyses (PRISMA) guidelinesPreferred Reporting ItemsManual literature searchesConclusionsElderly patientsAnal anastomosisEffect of ageResultsA totalEligible studiesFactor antagonists